Differential Controls Over Tactile Detection in Humans by Motor Commands and Peripheral Reafference
Bibliographic record
Abstract
The purpose of this study was to determine the extent to which motor commands and peripheral reafference differentially control the detection of near-threshold, tactile stimuli. Detection of weak electrical stimuli applied to the index finger (D2) was evaluated with two bias-free measures of sensory detection, the index of detectability (d') and the proportion of stimuli detected. Stimuli were presented at different delays prior to and during two motor tasks, D2 abduction, and elbow extension; both tasks were tested in two modes, active and passive. For both active tasks, the peak decrease in tactile suppression occurred at the onset of electromyographic activity. The time course for the suppression of detection during active and passive D2 abduction was identical, and preceded the onset of movement (respectively, -35 and -47 ms). These results suggest that movement reafference alone, acting through a mechanism of backward masking, could explain the modulation seen with D2 movement. In contrast, tactile suppression was significantly earlier for active elbow movements (-59 ms) as compared with passive (-21 ms), an observation consistent with both the motor command and peripheral reafference contributing to the suppression of detection of stimuli applied to D2 during movements about a proximal joint. A role for the motor command in tactile gating during distal movements cannot be discounted, however, because differences in the strength and distribution of the peripheral reafference may also have contributed to the proximo-distal differences in the timing of the suppression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".